docstr: extracts encoders from parameters at model creation
docstr_tok: ["extracts","encoders","from","parameters","at","model","creation"]
code: def helper_encoder_from_model(self, add_encoder_dict):
      """extracts encoders from parameters at model creation"""
      model = TFTModel(
          input_chunk_length=self.input_chunk_length,
          output_chunk_length=self.output_chunk_length,
          add_encoders=add_encoder_dict,
      )
  
      encoders = model.initialize_encoders()
      # see if encoding works
      _ = encoders.encode_train(
          self.target_multi,
          self.covariates_multi,
          self.covariates_multi,
      )
      _ = encoders.encode_inference(
          3,
          self.target_multi,
          self.covariates_multi,
          self.covariates_multi,
      )
      _ = encoders.encode_train_inference(
          3,
          self.target_multi,
          self.covariates_multi,
          self.covariates_multi,
      )
      return encoders
  
imports: import copy
  from typing import Optional, Sequence
  import numpy
  import pandas
  import pytest
  from darts import TimeSeries
  from darts.dataprocessing.encoders import FutureCallableIndexEncoder, FutureCyclicEncoder, FutureDatetimeAttributeEncoder, FutureIntegerIndexEncoder, PastCallableIndexEncoder, PastCyclicEncoder, PastDatetimeAttributeEncoder, PastIntegerIndexEncoder, SequentialEncoder
  from darts.dataprocessing.encoders.encoder_base import PastCovariatesIndexGenerator, SingleEncoder
  from darts.dataprocessing.encoders.encoders import CallableIndexEncoder, CyclicTemporalEncoder, DatetimeAttributeEncoder, IntegerIndexEncoder
  from darts.dataprocessing.transformers import Scaler
  from darts.logging import get_logger, raise_log
  from darts.utils import timeseries_generation
label_desc: Use image node_id 5 for calling the TestEncoder obj's underlying member method code with example usage: obj.helper_encoder_from_model(add_encoder_dict) and returns: encoders